We are now writing the introductory chapter in TechAmerica’s research examining the nexus of social, mobile, analytics, and cloud. CSC is hosting a #SpeedIdeation event with an open discussion on the changing role of government, the meaning of national security, and our entire approach to intelligence collection and law enforcement.

I came up as a software developer and only recently have I gotten into data science. Software engineering is in my bones, but for many of my colleagues, software engineering is a bit of a mystery. That’s a problem because it affects productivity. Data scientists need software engineering skill — just not all the skills a professional software engineer needs. So what are the essential software engineering practices needed in data science?

In this #SpeedIdeation I’m calling on all software engineers, hackers, and data scientists to brainstorm on the best practices needed to write solid code in data science.

The simulation anticipates that the Netflix model can become profitable with much less investment than is required for the Blockbuster model; and it anticipates that the Netflix model has a greater overall revenue potential than the Blockbuster model [Des]:

The simulation predicts a period of similar performance, followed by a period where Netflix far outperforms Blockbuster, followed by another period of similar performance. Gross profit for Netflix and Blockbuster shows agreement between simulation and actual performance:

Figure 3: Gross profit of Blockbuster and Netflix from 1998 to 2008 [Top]

The method was useful in simulating real innovation, it made predictions matched observations, it produced real insight, and was easy to build using R.